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  11. Robust Occlusion-aware Orbital Angular Momentum Feature Extraction Via All-optical Diffractive Processing Systems

Robust occlusion-aware orbital angular momentum feature extraction via all-optical diffractive processing systems

Keyao Li, Yuetian Jia, Min Gu

    Optics Express
    |June 14, 2025

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a robust optical neural network using orbital angular momentum (OAM) for enhanced feature extraction. The OAM-based diffractive optical neural network (DONN) demonstrates resilience to occlusions without retraining, improving optical computing reliability.

    Area of Science:

    • Optical Computing
    • Artificial Intelligence
    • Photonics

    Background:

    • Diffractive optical neural networks (DONNs) offer high-speed, energy-efficient optical information processing.
    • Conventional DONNs lack robustness against occlusions and distortions, limiting their real-world applicability.
    • Orbital Angular Momentum (OAM) offers unique properties for optical information encoding.

    Purpose of the Study:

    • To develop a robust all-optical feature extraction framework for DONNs using OAM.
    • To enhance the resilience of optical computing architectures to occlusions and environmental uncertainties.
    • To enable stable and efficient information representation in the OAM domain for improved DONN performance.

    Main Methods:

    • Proposed a novel feature extraction framework utilizing Orbital Angular Momentum (OAM).

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  • Employed a deep learning-trained diffractive processing framework to convert optical information into OAM modes.
  • Investigated the self-adaptive, occlusion-immune capabilities of the OAM-based DONN.

    • Main Results:

    • The OAM-based DONN framework demonstrated high performance across diverse and irregular occlusions.
    • The system maintained accuracy without requiring additional network retraining or samples.
    • Achieved stable and efficient information representation in the OAM domain.

    Conclusions:

    • The OAM-based approach significantly enhances the robustness and scalability of DONNs.
    • This method enables reliable optical computing in dynamic, occlusion-rich environments.
    • Paves the way for next-generation optical computing systems with improved real-time processing capabilities.